precsize: Sample size determination using power and precision analysis

Description

It determines the ratio between the sample size of power analysis and precision analysis analysis and also, give the required sample sizes.

Usage

precsize(pR, pT, sigr, sigt, c, alpha, beta)

Arguments

Incidence rate for the reference group

Incidence rate for the test group

sigr

Variability in the reference group

sigt

Variability in the test group

Constant value for allowance of maximum error margin

alpha

Level of significance

beta

The probability of type-II error

Value

precsize returns 3 values:

R: Ratio between the sample size of power analysis and precision analysis
n.power: Sample size required for power analysis
n.precision: Sample size required for precision analysis

Details

A pre-study power analysis for sample size determination is usually performed to calculate an appropriate sample size for achieving a desired power for detecting a clinically meaningful difference at a prespecified level of significance. In practice, a much larger sample size is expected for detecting a relatively smaller difference, especially for clinical trials with extremely low incidence rate. As a result, sample size determination based on power analysis may not be feasible. So, it is a good suggestion to determine the sample size based on precision analysis.

Examples

Run this code

# NOT RUN {
# The incidence rate of reference group is pR = 0.8 per thousands and that of test group
# is pT = 0.7 per thousands. It is also assumed that  the respective stanadard deviation
# of reference and test group are sigr = 2 and sigt = 1 respectively. The constant value
# is chosen c = 0.08 to allow the  maximum marginal error. The  level of significance is
# alpha = 0.05 and the probability of type-II error is beta = 0.20.

precsize(pR = 0.8, pT = 0.7, sigr = 2, sigt = 1, c = 0.08, alpha = 0.05, beta = 0.20)

# }